Manufacturers lose a significant portion of their sales earnings each year to warranty and maintenance costs. Products such as computer equipment are made of numerous components that require multi-step assemblies, servicing, configuration, and maintenance. Defects in these areas increase inefficiencies in the product's lifecycle.
Manufacturers have attempted to minimize these inefficiencies. Teams from different backgrounds with diverse resources are often cross-referenced to manage cost and quality of a product. For example, in original equipment manufacturing, quality standard sharing, testing programs, and periodic reviews between departments and companies are used for quality management. While some success has been achieved, these processes generally deal with quality issues after the fact.
Because the end products are often highly complex, each component of a product may have an unexpected effect on other components of the product. In the computer industry, for example, consumers often return faulty equipment to manufacturers as provided in their sales warranties. Returned equipment is then diagnosed and returned to originators of the suspected component. Each supplier tests the returned component, but there is often nothing wrong with the component itself. Instead, equipment failure may have been caused by a particular configuration of the product. It may have been due to an incompatible hardware-hardware configuration, a hardware-software configuration, and/or a software-software configuration. The exact circumstances leading to the failure, however, is not easily duplicated after the fact. Because the exact source of the failure goes undetected, no correction is made to the production process.
Information from disparate systems and processes are generally not captured or retained for further analysis through the lifecycle of a product. Various stages are involved in the lifecycle of a product. They include, among others, manufacture, assembly, storage, software and hardware configuration, shipping and delivery, handling, sales, customer use, warranty service, and field service. Various information from disparate systems are available but are not utilized effectively.
Thus, there is a need to compile related data from disparate systems, to analyze the data in real time, and to provide analysis and decision-making support across the entire chain of product and service delivery. There is a need for a closed-loop quality information system that coordinates data captured through a complex production system's full lifecycle. There is a need for consolidating information from various component information, various configuration information, and various field information. There is a need for such data to improve management of product performance, management of service performance, management of supplier performance, analysis of performance results, identification of deviations from baseline activity, identification of root causes of deviations, feedback to engineering, feedback to manufacturing, feedback to service, improvements to quality visibility, and measurements of effectiveness of quality and cost efforts.
In an alternative method performed by the system for improving management of quality and cost of a product throughout the product lifecycle of the product, a method for providing reiterative feed back to the system from a user is provided. The method comprises the steps of: capturing atomic level data on component information, configuration information, and field information from one or more parties involved throughout the product lifecycle; compiling one or more subsets of data from the atomic level data; performing cost or quality analysis on the subset to determine first analysis results; providing the first analysis results to a user for identifying patterns, associations or correlation in the first analysis results; delivering warning notifications based on the first analysis results; based at least in part on the identified patterns, associations or correlation, receiving feedback data from the user; based on the feedback data, further analyzing detailed data in the atomic level data to produce second analysis results; and presenting the second analysis results.